Papers by Hongxu Hou
Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages. |
| Approach: | They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources. |
| Outcome: | The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively. |
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)
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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao
| Challenge: | Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding. |
| Approach: | They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language. |
Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise (P19-2)
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| Challenge: | Existing models for Mongolian-Chinese translation are based on recurrent, convolutional neural networks or completely eliminate recurrence connections. |
| Approach: | They propose a adversarial training model to alleviate the UNK problem in Mongolian-Chinese machine translation by adding a screener to the model to emphasize the added Mongolian morphological noise. |
| Outcome: | The proposed model reduces training time and improves accuracy in Mongolian-Chinese translation tasks. |